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DEGREE REGULATIONS & PROGRAMMES OF STUDY 2022/2023

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DRPS : Course Catalogue : School of Mathematics : Mathematics

Undergraduate Course: Theory of Statistical Inference (MATH10028)

Course Outline
SchoolSchool of Mathematics CollegeCollege of Science and Engineering
Credit level (Normal year taken)SCQF Level 10 (Year 4 Undergraduate) AvailabilityAvailable to all students
SCQF Credits10 ECTS Credits5
SummaryIn this course we will develop mathematical aspects of statistical inference. The theory covered provides a greater understanding of the fundamental properties of popular statistical techniques and provides a framework for deriving procedures in more complex situations.
Course description Topics to be covered include:
1. Parametric families and likelihood.
2. Statistics, Sufficiency and Minimal Sufficiency.
3. Estimation, Unbiasedness, Efficiency, MVUE, Rao--Blackwell Theorem, Cramer--Rao Lower Bound.
4. Hypothesis testing, Neyman--Pearson Lemma.
5. Confidence Intervals, Pivots
6. Decision theory and admissibility of estimators.
7. Shrinkage/James Stein estimators.
8. Selected topics in modern statistics.
Entry Requirements (not applicable to Visiting Students)
Pre-requisites Students MUST have passed: Several Variable Calculus and Differential Equations (MATH08063) AND Fundamentals of Pure Mathematics (MATH08064) AND Statistical Methodology (MATH10095)
Co-requisites
Prohibited Combinations Other requirements None
Information for Visiting Students
Pre-requisitesVisiting students are advised to check that they have studied the material covered in the syllabus of each pre-requisite course before enrolling.
High Demand Course? Yes
Course Delivery Information
Academic year 2022/23, Available to all students (SV1) Quota:  None
Course Start Semester 1
Timetable Timetable
Learning and Teaching activities (Further Info) Total Hours: 100 ( Lecture Hours 22, Seminar/Tutorial Hours 5, Summative Assessment Hours 2, Programme Level Learning and Teaching Hours 2, Directed Learning and Independent Learning Hours 69 )
Assessment (Further Info) Written Exam 95 %, Coursework 5 %, Practical Exam 0 %
Additional Information (Assessment) Coursework 5%, Examination 95%
Feedback Not entered
Exam Information
Exam Diet Paper Name Hours & Minutes
Main Exam Diet S1 (December)2:00
Learning Outcomes
On completion of this course, the student will be able to:
  1. Write down formal definitions of statistical properties
  2. State and prove standard theoretical results in statistical inference
  3. Construct estimators, hypothesis tests and confidence intervals which satisfy desirable statistical properties
  4. Apply statistical theorems in examples to ascertain the properties of particular estimators, hypothesis tests and confidence intervals
Reading List
None
Additional Information
Course URL https://info.maths.ed.ac.uk/teaching.html
Graduate Attributes and Skills Not entered
KeywordsTSI
Contacts
Course organiserDr Timothy Cannings
Tel:
Email: Timothy.Cannings@ed.ac.uk
Course secretaryMrs Alison Fairgrieve
Tel: (0131 6)50 5045
Email: Alison.Fairgrieve@ed.ac.uk
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